settings = _loader.settings_loader(name, skylab_bins=use_skylab_bins)[name]
llh_model = EnergyLLH(**settings["llh_model_opts"])
llh = PointSourceLLH(exp, mc, livetime, llh_model, scramble=True,
**settings["llh_opts"])
multillh.add_sample(name, llh)
del exp
del mc
gc.collect()
# Do background only trials
print(":: Starting {} background trials ::".format(ntrials))
_t0 = time()
trials = multillh.do_trials(ntrials, src_ra=ra, src_dec=dec, src_w=w)
print(":: Done. {} ::".format(sec2str(time() - _t0)))
# Save trials
out = {
"ts": trials["TS"].tolist(),
"ns": trials["nsources"].tolist(),
"gamma": trials["gamma"].tolist(),
"rnd_seed": rnd_seed,
"ntrials": ntrials,
}
_fname = os.path.join(outpath, "job_{}.json.gz".format(job_id))
with gzip.open(_fname, "w") as fp:
json.dump(out, fp=fp, indent=2)
print("Saved to:\n {}".format(_fname))
elif years == 1:
print("Using IC86")
llh = datascript.init86I(mode='box')
if catalog:
if years == 1:
bckg_trials = PointSourceLLH.do_trials(
llh[0],
n_iter=batchsize,
src_ra=src_ra,
src_dec=src_dec,
src_w=modelweights['{}'.format(llhweight)])
else:
bckg_trials = MultiPointSourceLLH.do_trials(
llh[0],
n_iter=batchsize,
src_ra=src_ra,
src_dec=src_dec,
src_w=modelweights['{}'.format(llhweight)])
else:
if years == 1:
bckg_trials = PointSourceLLH.do_trials(llh[0],
n_iter=batchsize,
src_ra=src_ra,
src_dec=src_dec)
else:
bckg_trials = MultiPointSourceLLH.do_trials(llh[0],
n_iter=batchsize,
src_ra=src_ra,
src_dec=src_dec)
#choose an output dir, and make sure it exists